The diversity and abundance of North American butterflies vary with habitat disturbance and geography
Bibliographic record
Abstract
Abstract Aim We used data from the annual Fourth of July Butterfly Count for the years 1989–97 to examine patterns of species richness and total butterfly abundance across North America and within topographically diverse and disturbed landscapes. Location We analysed counts from 514 different locations in North America. The counts represent all areas of the USA and southern Canada, with a few Mexican sites as well, although most counts were in the eastern USA. Methods First, we standardized published count data according to the effort expended per count (total party‐hours). Using regression analysis and analysis of variance, we then examined the impact of latitude, longitude, topographical relief, habitat disturbance and different climatic measures on the species richness and total abundance of butterflies per count. We also examined the abundance of exotic species in disturbed landscapes. Results Our analyses suggest that: (1) species richness is highest at low latitudes and near Rocky Mountain longitudes; (2) the total abundance of individuals is highest in northern US latitudes and Great Plains longitudes; (3) species richness but not total abundance increases with greater topographical relief; (4) species richness and diversity indices are lower in more disturbed habitats; and (5) the abundance of the introduced Pieris rapae (L.) is greater in more disturbed habitats. Main conclusions Different factors control the abundance and species richness of North American butterflies. Along with geographical location, habitat disturbance and topographical variability affect species richness. Our analysis also shows the value of broad‐based monitoring regimes, such as the North American Fourth of July Butterfly Count.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".